41 citations · 112 across the 21 of their papers we have counts for
23 papers
What Should We Measure Next? Finding Identification Strategies by Refining Mechanisms
Mikko Väänänen, Fanyu Cui, Carlos Cinelli +1
Canonical approaches in causal inference treat model specification as fixed, assuming that researchers directly translate all relevant domain knowledge into a causal model, which c…
Invariance of Clustering Operations in Causal Effect Identification
Jani Nykänen, Otto Tabell, Santtu Tikka +1
Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial causal relations among variable…
Early Warning Signals Appear Long Before Dropping Out: An Idiographic Approach Grounded in Complex Dynamic Systems Theory
Mohammed Saqr, Sonsoles López-Pernas, Santtu Tikka +1
The ability to sustain engagement and recover from setbacks (i.e., resilience) -- is fundamental for learning. When resilience weakens, students are at risk of disengagement and ma…
Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwise remain non-identifiable. Although id…
Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes
Mohammed Saqr, Sonsoles López-Pernas, Tiina Törmänen +3
This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation…
Monotone Missing Data: A Blessing and a Curse
Santtu Tikka, Juha Karvanen
Monotone missingness is commonly encountered in practice when a missing measurement compels another measurement to be missing. Because of the simpler missing data pattern, monotone…